Bayesian entropy for spatial sampling design of environmental data

Determination of Optimal Sampling Design for Spatial Data Analysis

Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here]

Bayesian Spatial Sampling Design

This paper presents a model-based approach to the problem of the optimal choice of a spatial sampling design in the presence of uncertainty about the distribution of the observations. Our approach, which is based on the polar spectral decomposition of stationary isotropic random fields, makes it possible to represent the spatial error process by a linear regression model with (infinitely many) ...

Bayesian Prediction in Spatial Data Analysis

Bayesian Analysis of Survival Data with Spatial Correlation

Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study‎. ‎One of the most important issues in the analysis of survival data with spatial dependence‎, ‎is estimation of the parameters and prediction of the unknown values in known sites based on observations vector‎. ‎In this paper to analyze this type of survival‎, ‎Cox...

The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model

Statistical modeling methods are based on multivariate regression methods and require the presence and absence location of data for the construction of the model. In most cases, there is no trustworthy absence data. Therefore, other methods that are based only on the presence of the phenomenon are used. Considering the importance of modeling - saving time and cost and the probable prediction of...